Analysis of Patient Groups and Immunization Results Based on Subspace Clustering

Abstract

Abstract. Biomedical experts are increasingly confronted with what is often called Big Data, an important subclass of high-dimensional data. High-dimensional data analysis can be helpful in finding relationships between records and dimensions. However, due to data complexity, experts are decreasingly capable of dealing with increasingly complex data. Mapping higher dimensional data to a smaller number of relevant dimensions is a big challenge due to the curse of dimensionality. Irrelevant, redundant, and conflicting dimensions affect the effectiveness and efficiency of analysis. Furthermore, the possible mappings from highto low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We show the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we analyze relationships between patients, sets of patient attributes, and outcomes of a vaccination treatment by means of a subspace clustering approach. We present an analysis workflow and discuss future directions for high-dimensional (medical) data analysis and visual exploration.

Computational Health Informatics in the Big Data Age

“…It partitions datasets along with the various subsets of dimension instead of the whole space, to overcome the challenge of curse of dimensionality in big data analysis [Kriegel et al 2009]. Hund et al [2015] apply a subspace clustering approach to the real-world medical data and analyze the patient data relationship and immunization treatment. Experimental results show how subspace clustering can effectively identify grouping of patients compared with a full space analysis such as hierarchical clustering.…”

On the Challenges and Opportunities in Visualization for Machine Learning and Knowledge Extraction: A Research Agenda

“…Consequently, iML-approaches can be beneficial to support finding solutions in hard biomedical problems [13].…”

Machine Learning for Health Informatics

“…Here the doctor-in-the-loop can help, where human expertise and long-term experience can assist in solving problems which otherwise would remain NP-hard; examples include subspace clustering [48], protein folding [49], or privacy preserving ML, which is an important issue, fostered by anonymization, in which a record is released only if it is indistinguishable from k other entities in the data, but where k-anonymity is highly dependent on spatial locality in order to effectively implement the technique in a statistically robust way. In high dimensionalities data becomes sparse, hence the concept of spatial locality is not easy to define.…”

“…What is recognized as comfort for end-users of individual systems, can be applied in scientific research for the interactive exploration of high-dimensional data sets [96]. Consequently, iML-approaches can be beneficial to support finding solutions in hard biomedical problems [48]. Actually, humans are quite good in comparison for the determination of similarities and dissimilarities -described by nonlinear multidimensional scaling (MDS) models [97].…”